In high-dimensional sparse regression, the \textsc{ Lasso} estimator offers excellent theoretical guarantees but is well-known to produce biased estimates. To address this, \cite{ Javanmard2014} introduced a method to ``debias" the \textsc{ Lasso} estimates for a random sub-Gaussian sensing matrix $\boldsymbol{ A}$. Their approach relies on computing an ``approximate inverse" $\boldsymbol{ M}$ of the matrix $\boldsymbol{ A}^\top \boldsymbol{ A}/n$ by solving a convex optimization problem. This matrix $\boldsymbol{ M}$ plays a critical role in mitigating bias and allowing for construction of confidence intervals using the debiased \textsc{ Lasso} estimates. However the computation of $\boldsymbol{ M}$ is expensive in practice as it requires iterative optimization. In the presented work, we re-parameterize the optimization problem to compute a ``debiasing matrix" $\boldsymbol{ W} := \boldsymbol{ AM}^{ \top}$ directly, rather than the approximate inverse $\boldsymbol{ M}$. This reformulation retains the theoretical guarantees of the debiased \textsc{ Lasso} estimates, as they depend on the \emph{ product} $\boldsymbol{ AM}^{ \top}$ rather than on $\boldsymbol{ M}$ alone. Notably, we provide a simple, computationally efficient, closed-form solution for $\boldsymbol{ W}$ under similar conditions for the sensing matrix $\boldsymbol{ A}$ used in the original debiasing formulation, with an additional condition that the elements of every row of $\boldsymbol{ A}$ have uncorrelated entries. Also, the optimization problem based on $\boldsymbol{ W}$ guarantees a unique optimal solution, unlike the original formulation based on $\boldsymbol{ M}$. We verify our main result with numerical simulations.
Categories: stat.ML cs.LG